Obesity is an important risk factor for atherosclerosis. However, the reasons for the relationship between these disorders are still poorly understood. Evidence from body composition studies suggests that adiposity is highly correlated with several important endocrine measures including phenotypes related to glucose and lipoprotein metabolism. Little is known regarding the genes that influence adiposity and their pleiotropic effects on these endocrine parameters and on correlated risk factors for atherosclerosis such as lipoprotein phenotypes. In this Project, we will measure several adiposity-related phenotypes including total body fat (estimated using bioimpedance), and serum concentrations of several adipocyte derived endocrine factors (e.g., leptin, adiponectin, acylation stimulating protein, and TNFalpha) in pedigreed baboons. To better examine the underlying genetic determinants of variable gene expression, we will also continue to measure quantitative mRNA levels of several candidate genes (the leptin, lipoprotein lipase, and glucose transporter 4 genes) as well as three additional genes (the leptin receptor, adiponectin, and resistin genes) in biopsied omental fat tissue. We will detect and localize quantitative trait loci (QTLs) influencing adiposity-related phenotypes and test hypotheses regarding their pleiotropic effects on lipoprotein traits and genotype ? age interaction. Localization of quantitative trait loci will be accomplished via a genomic screen using candidate gene and STR polymorphisms in a single pedigree of 750 non-inbred baboons. Statistical linkage analyses will be performed using the multipoint variance component method which makes efficient use of all available information and which we have extended to accommodate the complications of inbred pedigrees. When a quantitative trait locus is found, we will utilize multivariate linkage analysis to determine if adiposity-related genes have pleiotropic effects on lipoprotein phenotypes. Finally we will attempt to identify strong positional candidate genes in the regions of promising QTLs and will use combined linkage/disequilibrium analyses and a novel Bayesian quantitative trait nucleotide analysis method to assess whether polymorphisms in these genes account for the observed linkage signal.